Research From Columbia Business School Reveals How To Shorten Emergency Room Wait Time

According to the U.S. Department of Health and Human Services, demand for emergency health care services is rapidly increasing, causing over-crowding and long wait-times in emergency rooms nationwide. New research from Columbia Business School shows that predictive analytics – that is, using data about ER demand to predict future demand – could help hospitals reduce wait times and improve care by diverting patients away from emergency rooms before they become overcrowded.

Hospital diversions are intended to help patients get care faster by directing them away from overcrowded ERs and toward facilities that can care for them more appropriately and quickly. In current practice, diversion decisions are typically made based solely on information about current congestion — i.e. if a maximum threshold is reached, then new patients will be diverted. However, the researchers suggest that by using predictions of when patient congestion is likely to build, hospitals could substantially reduce the wait times of patients seeking medical care from an ER.

“Patients on their way to the emergency room want to know that their emergency is going to be handled as expeditiously as possible,” said Professor Carri Chan, co-author of the study and Sidney Taurel Associate Professor of Business at Columbia Business School. “By using predictive modeling to develop more effective diversion policies, hospitals can reduce wait times for patients by up to 15 percent, improving care and customer satisfaction while at the same time saving time and money.”

Chan concluded: “Using predictive analytics is a step towards eliminating the over-crowding and long wait times that plague may of today’s emergency rooms, ensuring patients receive the care they need when they need it.”

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